from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.631	0.0	-1	1	0.050	0.002	0.240	0.240	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.524	0.0	-1	5	0.050	0.000	0.246	0.246	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.779	0.0	1	100	0.049	0.001	0.242	0.242	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.000	6.380	0.0	-1	100	0.050	0.000	0.250	0.250	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.508	0.0	1	5	0.050	0.001	0.248	0.248	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.000	6.365	0.0	1	1	0.050	0.001	0.249	0.249	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.001	0.319	0.0	-1	1	0.008	0.000	0.603	0.604	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.380	0.0	-1	5	0.008	0.000	0.499	0.499	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.001	0.346	0.0	1	100	0.009	0.000	0.537	0.537	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.366	0.0	-1	100	0.008	0.000	0.532	0.532	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.376	0.0	1	5	0.008	0.000	0.507	0.507	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.377	0.0	1	1	0.008	0.000	0.510	0.511	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.904	0.125	0.000	0.002	-1	1	0.173	0.006	11.001	11.007	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.003	0.000	0.026	-1	1	0.009	0.000	2.903	2.904	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.694	0.026	0.000	0.003	-1	5	0.172	0.004	15.633	15.636	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.027	0.003	0.000	0.027	-1	5	0.009	0.000	3.018	3.020	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.058	0.003	0.000	0.002	1	100	0.208	0.001	9.904	9.905	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.000	0.000	0.024	1	100	0.010	0.000	2.457	2.459	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.720	0.040	0.000	0.003	-1	100	0.208	0.002	13.093	13.093	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.027	0.002	0.000	0.027	-1	100	0.009	0.001	2.856	2.871	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.053	0.004	0.000	0.002	1	5	0.171	0.001	12.004	12.004	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.001	0.000	0.024	1	5	0.009	0.000	2.587	2.588	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.211	0.008	0.001	0.001	1	1	0.170	0.001	7.115	7.115	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.001	0.000	0.025	1	1	0.009	0.000	2.689	2.693	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.752	0.022	0.000	0.002	-1	1	0.026	0.000	68.118	68.119	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.004	0.000	0.005	-1	1	0.001	0.000	7.320	7.348	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.642	0.039	0.000	0.003	-1	5	0.028	0.002	95.863	96.064	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.000	0.000	0.005	-1	5	0.001	0.000	7.269	7.326	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.000	0.009	0.000	0.002	1	100	0.061	0.002	32.874	32.889	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.955	3.975	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.676	0.029	0.000	0.003	-1	100	0.060	0.000	44.884	44.884	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	0.000	0.006	-1	100	0.001	0.000	7.652	7.712	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.959	0.007	0.000	0.002	1	5	0.027	0.000	73.330	73.330	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.446	4.462	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.085	0.009	0.000	0.001	1	1	0.026	0.000	41.727	41.727	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.702	2.719	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.066	0.076	0.026	0.0	-1	1	0.744	0.051	4.119	4.128	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.665	0.028	0.022	0.0	-1	5	0.726	0.004	5.046	5.046	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.637	0.067	0.022	0.0	1	100	0.699	0.014	5.201	5.202	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.605	0.093	0.022	0.0	-1	100	0.720	0.010	5.010	5.010	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.669	0.067	0.022	0.0	1	5	0.698	0.007	5.259	5.259	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.562	0.050	0.022	0.0	1	1	0.718	0.010	4.959	4.960	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.021	0.0	-1	1	0.003	0.002	0.256	0.295	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	5	0.002	0.002	0.244	0.311	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	100	0.001	0.000	0.610	0.614	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.000	0.648	0.649	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.029	0.0	1	5	0.001	0.000	0.662	0.663	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	1	0.001	0.000	0.640	0.641	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.798	0.983	0.000	0.001	-1	1	0.117	0.001	6.833	6.833	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	0.000	0.002	-1	1	0.000	0.000	9.646	10.185	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.996	0.300	0.000	0.001	-1	5	0.201	0.003	4.946	4.947	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.852	8.393	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.480	0.267	0.000	0.005	1	100	0.602	0.009	9.106	9.107	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	4.178	4.379	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.125	0.253	0.000	0.003	-1	100	0.600	0.013	5.207	5.208	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	7.193	7.562	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.649	0.172	0.000	0.002	1	5	0.207	0.006	7.964	7.967	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.789	4.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.936	0.237	0.000	0.001	1	1	0.114	0.002	8.223	8.225	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.882	4.138	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.011	0.001	0.000	-1	1	0.000	0.000	62.932	63.207	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	23.553	24.699	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.001	0.001	0.000	-1	5	0.001	0.000	36.320	36.343	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	23.701	24.738	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.036	0.007	0.000	0.000	1	100	0.004	0.000	7.919	7.920	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	6.134	6.357	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.038	0.006	0.000	0.000	-1	100	0.005	0.002	7.293	7.631	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	24.304	25.085	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.001	0.000	31.505	31.536	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.379	6.654	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	1	0.000	0.000	48.853	48.964	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.567	6.864	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.517	0.061	30	0.031	0.0	random	0.380	0.025	1.359	1.362	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.560	0.013	30	0.029	0.0	k-means++	0.408	0.026	1.374	1.376	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.779	0.192	30	0.138	0.0	random	2.835	0.018	2.038	2.038	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.941	0.039	30	0.135	0.0	k-means++	3.053	0.038	1.946	1.946	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	random	0.0	0.0	9.239	13.497	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.601	13.347	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	k-means++	0.0	0.0	12.421	14.174	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	13.469	14.253	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.483	0.000	random	0.0	0.0	6.843	7.363	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	12.755	13.186	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.500	0.000	k-means++	0.0	0.0	6.585	7.140	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	11.149	11.489	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	adjusted_rand_score_sklearn	adjusted_rand_score_sklearnex	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	0.000126	-0.000965	0.001090
4	KMeans_short	predict	10000	1000	2	0.001245	-0.000750	0.001995
7	KMeans_short	predict	10000	1000	100	0.278733	0.293767	0.015034
10	KMeans_short	predict	10000	1000	100	0.317011	0.256968	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.075	0.001	20	0.002	0.0	random	0.026	0.001	2.896	2.900	See	See
3	KMeans_short	fit	10000	10000	2	0.217	0.006	20	0.001	0.0	k-means++	0.081	0.000	2.690	2.690	See	See
6	KMeans_short	fit	10000	10000	100	0.199	0.009	20	0.040	0.0	random	0.106	0.001	1.888	1.888	See	See
9	KMeans_short	fit	10000	10000	100	0.560	0.007	20	0.014	0.0	k-means++	0.312	0.003	1.793	1.793	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.0	4.351	4.358	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	13.511	13.851	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	4.073	4.094	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	12.465	12.902	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.317	0.000	random	0.001	0.0	2.614	2.625	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	10.515	10.739	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.323	0.000	k-means++	0.001	0.0	2.658	2.666	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	9.347	9.542	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	0.56	0.55	0.01
4	LogisticRegression	predict	1000	100	10000	0.35	0.28	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.074	0.444	[20]	0.072	0.000	2.087	0.030	5.307	5.308	See	See
3	LogisticRegression	fit	1000	1000	10000	0.940	0.584	[26]	0.085	0.001	0.841	0.019	1.119	1.119	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.136	0.0	0.001	0.001	0.581	1.124	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.016	0.0	0.000	0.000	0.358	0.364	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	5.281	0.0	0.005	0.001	0.293	0.296	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.065	0.0	0.001	0.000	0.052	0.053	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	r2_score_sklearn	r2_score_sklearnex	diff_r2_scores
1	Ridge	predict	1000	1000	10000	0.082567	0.122191	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.178	0.003	0.448	0.0	0.189	0.001	0.946	0.946	See	See
3	Ridge	fit	1000000	1000000	100	1.173	0.063	0.682	0.0	0.312	0.239	3.765	4.748	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.011	0.001	6.964	0.0	0.019	0.001	0.601	0.601	See	See
2	Ridge	predict	1000	1	10000	0.000	0.000	1.291	0.0	0.000	0.000	0.686	0.748	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.000	5.670	0.0	0.000	0.000	0.549	0.724	See	See
5	Ridge	predict	1000000	1	100	0.000	0.000	0.017	0.0	0.000	0.000	0.603	0.636	See	See